CN112487287A - Method for recommending severe illness risk by using gene detection result and questionnaire - Google Patents
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Abstract
The invention discloses a method for recommending severe illness danger by using gene detection results and questionnaires, which comprises the following specific steps: s1, calculating the gene risk value of the user and the disease related to the serious illness by using a gene detection technology to obtain a gene risk label; s2, using the questionnaire to make the user fill in the questions related to the serious illness and obtain questionnaire labels; s3, obtaining a comprehensive label according to the gene risk value of the user and the questionnaire result; s4, making a heavy disease label for each sold heavy disease according to the characteristics of the guaranteed disease; s5, calculating the matching score of each heavy disease label and the user according to the comprehensive label of the user; and S6, ranking the heavy disease labels according to the matching scores from high to low, and giving out the suggested ranking suitable for the heavy disease of the user. The invention widens the application scene of gene detection, simultaneously develops the recommendation mode of insurance, and truly combines the disease risk of the user to recommend the most suitable personalized product.
Description
Technical Field
The invention relates to a method for recommending severe illness by using gene detection results and questionnaires, and relates to the fields of insurance and computers.
Background
Due to the development of technology, the price of gene detection is reduced, and more people are exposed to gene detection. Due to concerns about their health, many people have acquired genetic health risk information through genetic testing. However, most of the participators only pay attention to diseases with high health risk after knowing the health risk of the participators, and some preventive measures can be taken without further application of the gene detection result.
The insurance industry is gradually accepted by people after years of development. But because insurance knowledge is relatively more professional and insurance terms are obscure, users have a low understanding of insurance products before purchasing them, which increases the difficulty of purchasing them; also, there is no concept as to what insurance products a user needs to purchase for his or her home condition and income level, so a suitable customized insurance solution is a user pain point.
Therefore, how to select products meeting the requirements according to gene detection and generate products meeting the requirements or a product combination scheme becomes a problem to be solved urgently in the whole insurance field.
Disclosure of Invention
In view of the existing technical problems, the present invention provides a method for recommending a serious illness by using a gene detection result and a questionnaire, which can perform insurance recommendation by using the questionnaire in combination with the gene detection result, so as to improve insurance recommendation accuracy, user purchasing will and product experience of the user.
In order to achieve the above objects, the present invention provides a method for recommending a severe risk using a gene test result and a questionnaire, comprising the following steps:
and S1, calculating the gene risk value of the disease related to the severe risk of the user by using a gene detection technology to obtain a gene risk label.
In the technical scheme, the used gene detection technology can be a chip detection technology and can also be a whole genome sequencing technology, the type of the gene detection technology is not limited, and only the gene risk value of the related disease needs to be obtained. The Genetic Risk value, also called Polygenic Risk Score (PRS), sometimes called Genetic Risk Score (Genetic Risk Score, GRS), is mainly used to assess the Genetic Risk of an individual suffering from a disease and is calculated from the genotypic effect value of GWAS statistics. The calculation formula is as follows:
wherein i represents SNP sites, m represents the total number of the SNP sites, beta represents the effect of the SNP sites on diseases, j represents the genotype of the SNP sites, 0, 1 and 2 are respectively used for characterizing no mutation, heterozygous mutation and homozygous mutation, and omega represents the frequency of each genotype.
Further, in step S1, the method for obtaining the gene risk signature is as follows:
(S1-1) if the user carries a risk mutation of a certain disease, obtaining a gene risk signature of the disease;
(S1-2) if the gene of the user calculates that the gene risk value of a certain disease belongs to the top 10% of the population, acquiring the gene risk label of the disease.
S2, using the questionnaire, the user fills in the questions related to the sale of the major illness, and obtains the questionnaire label.
Further, the questionnaire includes the following questions: birthday, sex, height, weight, blood type, behavior habit, eating habit, health problem, cancer of the immediate relatives;
(S2-1) calculating the age of the user by the birthday to obtain labels of different age zones;
(S2-2) a label for calculating obesity or underweight by height and weight;
(S2-3) acquiring a behavior tag of good or bad health through the behavior habit;
(S2-4) acquiring a favorable or unhealthy diet label through the diet habit;
(S2-5) acquiring an existing disease label through the health issue;
(S2-6) obtaining a parent cancer signature by whether the parent has cancer.
And S3, obtaining a comprehensive label according to the gene risk value of the user and the questionnaire result.
Further, the method for obtaining the comprehensive label is as follows:
(S3-1) comprehensive high-risk lung cancer label: if the user has a gene risk label of lung cancer, or the person has smoking habit, or the close relatives of the person have the disease history of lung cancer, acquiring the label;
(S3-2) comprehensive cancer high risk signature: obtaining a label if the user has multiple genetic cancer risk labels;
(S3-3) synthesizing a high risk label for coronary heart disease: if the user has any label of the risk of hypertension, hypertriglyceridemia and coronary heart disease, obtaining the label;
(S3-4) synthetic motor neuron disease signature: the signature is obtained if the user has any signature carrying a dystonia risk mutation, tardive dyskinesia, essential tremor, amyotrophic lateral sclerosis, gene progressive supranuclear palsy.
And S4, making a heavy disease label for each heavy disease to be sold according to the characteristics of the guaranteed disease.
Further, the method for obtaining the label of the serious illness is as follows: arranging the sold severe risks and making a label for each severe risk, wherein the label comprises the following steps: suitable sex, weight range, age range, whether some diseases are carried, guaranteed disease type, and reimburseable times.
And S5, calculating the matching score of each severe risk label and the user according to the comprehensive label of the user.
Further, the method for calculating the matching degree of the severe risk label is as follows: setting the comprehensive label of the user as D and the comprehensive label conforming to a certain condition or a certain disease as Dn，DnCorresponding weight is Qn(ii) a Setting a heavy disease label as I, and setting a certain heavy disease label on sale as In(ii) a Setting the matching score of the label of the severe disease and the comprehensive label as S, InComprehensive label D of coveragenHas a matching score of Sn＝In(D1，D2，D3……Dn)；
Calculating the matching score S of a certain disease risk and the comprehensive labeln＝D1*Q1+D2*Q2+D3*Q3+……+Dn*QnI.e. covering D for the severe risk firstnSetting the weight QnAfter that, the summation is performed.
And S6, sequencing the various severe danger labels from high to low according to the matching scores, and giving out the recommended sequence of the severe danger suitable for the user.
Because the probability of different diseases of different people is different, the serious disease risk which can mainly ensure the diseases with higher comprehensive score is preferentially recommended according to the disease comprehensive score of the user which is ranked from high to low. And finally, sequencing all insurance according to the matching degree and displaying the insurance to the user, so that the insurance can cover the potential diseases with the highest comprehensive risk of the user.
In summary, compared with the prior art, the invention has the following technical advantages:
1. the recommendation of the serious insurance scheme to the user is realized by using the gene detection result and the questionnaire, so that the insurance scheme recommendation and purchase mode of the user group is changed.
2. The application scenes of gene detection are widened, and the most appropriate personalized products are recommended by really combining the disease risk of the user.
3. The insurance recommendation method is developed, insurance recommendation accuracy is improved, user purchase intention is improved, and product experience of the user is improved.
Drawings
FIG. 1 is a schematic flow chart of the present invention for recommending a severe risk.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method for recommending the severe illness by using the gene detection result and the questionnaire comprises the following specific steps:
s1: and (3) purchasing a gene detection service by the user, waiting for the gene risk value of the user related to the serious risk through gene calculation, and obtaining a gene risk label.
A collecting tool: for the gene data of the user, a high-throughput gene chip or a high-throughput sequencing instrument is used for obtaining the gene data. Questionnaire data of the user is collected using an electronic online questionnaire. The disease risk value is calculated and obtained by the algorithm of the existing company, and the calculated value is directly used as an input parameter. The method for obtaining the gene risk label of the disease related to the serious illness of the user is as follows:
(S1-1) if the user carries a risk mutation of a certain disease, obtaining a gene risk signature of the disease;
(S1-2) if the gene of the user calculates that the gene risk value of a certain disease belongs to the top 10% of the population, acquiring the gene risk label of the disease.
S2: the questionnaire is used to get questionnaire labels for the user to fill in questions related to the severe illness.
The questionnaire includes the following questions: birthday, sex, height, weight, blood type, behavioral habits (such as whether smoking, drinking, chewing areca, drinking coffee, sun drying, physical exercise, dangerous sports (diving, boxing, rock climbing, drifting and the like), taking addictive drugs, or none of the above), dietary habits (such as whether white meat (chicken, duck, fish, shrimp, crab and the like), red meat (pig, cattle, sheep and the like), pickled food, green leaf vegetables, fresh fruits, fried food, or none of the above is taken), health problems (such as whether hypertension, hepatitis B virus carrying, hepatitis B surface antigen positive, alcoholic liver disease, severe fatty liver, hyperlipidemia, or none of the above is taken), and the like, and the basic conditions of the orthotopic family suffering from cancer (such as whether a parent, brother, sister, or an ancestor a person suffers from cancer).
And then, marking a questionnaire label for the user according to the questionnaire result, wherein the method comprises the following steps:
(S2-1) calculating the age of the user by the birthday to obtain labels of different age zones;
(S2-2) a label for calculating obesity or underweight by height and weight;
(S2-3) acquiring a behavior tag of good or bad health through the behavior habit;
(S2-4) acquiring a favorable or unhealthy diet label through the diet habit;
(S2-5) acquiring an existing disease label through the health issue;
(S2-6) obtaining a parent cancer signature by whether the parent has cancer.
In the embodiment provided by the present invention, extensions of the meanings of the above problems and their labels can be extended for different application scenarios, and are not described herein again.
S3: and obtaining a comprehensive label of the user according to the gene risk value and the questionnaire result. The comprehensive label can be formed by different combinations according to the needs, and the method for obtaining the comprehensive label comprises the following steps:
(S3-1) comprehensive high-risk lung cancer label: if the user has a gene risk label of lung cancer, or the person has smoking habit, or the close relatives of the person have the disease history of lung cancer, acquiring the label;
(S3-2) comprehensive cancer high risk signature: obtaining a label if the user has multiple genetic cancer risk labels;
(S3-3) synthesizing a high risk label for coronary heart disease: if the user has any label of the risk of hypertension, hypertriglyceridemia and coronary heart disease, obtaining the label;
(S3-4) synthetic motor neuron disease signature: the signature is obtained if the user has any signature carrying a dystonia risk mutation, tardive dyskinesia, essential tremor, amyotrophic lateral sclerosis, gene progressive supranuclear palsy.
In the embodiment provided by the present invention, the meaning of each high-risk tag may be extended or the type of the integrated tag may be extended according to different application scenarios, and details are not repeated here.
S4: and (4) making a heavy disease label for each heavy disease being sold according to the characteristics of the guaranteed diseases.
Specifically, the method for sorting the sold severe risks and making a label for each severe risk comprises the following steps: suitable sex, weight range, age range, whether carrying some diseases, guaranteed disease types, reimburseable times and the like can also be extended and expanded.
S5: and calculating the matching scores of the various heavy disease labels and the user comprehensive label according to the comprehensive label.
The specific calculation method of the matching degree is as follows: setting the comprehensive label of the user as D and the comprehensive label conforming to a certain condition or a certain disease as Dn，DnCorresponding weight is Qn(ii) a Setting a heavy disease label as I, and setting a certain heavy disease label on sale as In(ii) a Setting the matching score of the label of the severe disease and the comprehensive label as S, InComprehensive label D of coveragenHas a matching score of Sn＝In(D1，D2，D3……Dn) (ii) a Calculating a certain risk and a comprehensive labelMatching score Sn＝D1*Q1+D2*Q2+D3*Q3+……+Dn*QnI.e. covering D for the severe risk firstnSetting the weight QnAfter that, the summation is performed.
For example, a user has liver cancer comprehensive risk D1Combined risk of lung cancer D ═ 32Overall risk of gastric cancer D ═ 2.53Thyroid cancer complex D241.9; setting the weight Q of liver cancer1Weight of lung cancer Q22Weight of gastric cancer Q1.83Weight of thyroid cancer Q1.34The above weight values may also be modified as needed.
Products for severe disease I1If the cancer includes liver cancer, lung cancer, stomach cancer, but not thyroid cancer, the user is relatively insurance I1Is matched with the score S1＝I1(D1，D2，D3)＝3*2+2.5*1.8+2*1.3＝13.1。
Products for severe disease I2The covered cancers comprise liver cancer, lung cancer, stomach cancer and thyroid cancer, and the user is relatively insurance I2Is matched with the score S2＝I2(D1，D2，D3，D4)＝3*2+2.5*1.8+2*1.3+1.9*1＝15。
And, a certain severe danger InCorresponding matching score SnThe higher the value of (c), the higher the match between the critical illness and the user.
S6: and (4) ranking the heavy disease labels according to the matching scores from high to low, and giving out the recommended ranking suitable for the heavy disease of the user.
Since the insurance application requirement and the guaranteed disease are different for each serious risk, the Sn value is different for each serious risk. And sorting all the severe risks according to Sn, and recommending the severe risks to the user from high to low. For example, the user recommended the 2 insurance products with scores of S1-13.1 and S2-15, respectively. Insurance I2 corresponding to a higher match score S2 will be recommended to the user as a higher recommendation priority product. And finally, giving out a final heavy disease recommendation suggestion according to the sequencing result. Therefore, blind marketing is avoided, the conflict emotion of the user is reduced, the user experience is improved, and efficient and accurate and personalized recommendation of insurance service is achieved.
Furthermore, the inventive method may be used for insurance customization APP, and the program code for performing the inventive operations may be written in one or more programming languages, or a combination thereof. The programming languages include an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely or partially on the user's computer, cell phone.
When the method is applied specifically, for example, a certain user firstly purchases gene detection service, and the gene risk value of the disease related to the severe risk is calculated by utilizing the insurance customization APP system to obtain the gene risk label.
Secondly, the user fills in the following information in the questionnaire of the insurance customization APP system: a30-year-old male is 170 in height and 80kg in weight, does not like exercise, has the habit of smoking and drinking and has relatives suffering from liver cancer.
Then, in the risk values calculated by gene detection, the risk of the user suffering from liver cancer ranks 10% in the top of all people, and the user obtains a comprehensive liver cancer high-risk label score of 25 (higher than the score) because the questionnaire knows that the user has a drinking habit and relatives have liver cancer; and because the questionnaire shows that the user has smoking habit, but the gene risk is not high and no relatives have the lung cancer, the obtained comprehensive lung cancer high-risk label score is 15 (the score is medium).
Then, the insurance customization APP system formulates a heavy risk label for the sold insurance according to the characteristics, wherein 1 heavy risk is aimed at middle-aged males, the extra 50% of the guarantee sum is paid to the liver cancer, and the weight of the insurance for the liver cancer is set to be 2; the heavy insurance pays 25% of the guarantee amount for the lung cancer, and the weight of the insurance for the liver cancer is set to be 1.8. Calculating and obtaining the matching score S of the severe disease to the user comprehensive label125 x 2+15 x 1.8+ (other heavy disease scores correspond to weights).
Finally, the insurance customization APP system calculates the matching scores S of all the heavy risks in the sale by using the calculation mode2，S3，S4… …, the heavy insurance is ranked from high to low according to the matching score to obtain an insurance recommendation scheme, and the top-ranked products can be preferentially recommended to the user.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application.
Claims (6)
1. A method for recommending severe illness by using gene detection results and questionnaires is characterized by comprising the following specific steps:
s1, calculating the gene risk value of the user and the disease related to the serious illness by using a gene detection technology to obtain a gene risk label;
s2, using the questionnaire to make the user fill in the questions related to the sold serious illness and obtain questionnaire labels;
s3, obtaining a comprehensive label according to the gene risk value of the user and the questionnaire result;
s4, making a heavy disease label for each sold heavy disease according to the characteristics of the guaranteed disease;
s5, calculating the matching score of each heavy disease label and the user according to the comprehensive label of the user;
and S6, sequencing the various severe danger labels from high to low according to the matching scores, and giving out the recommended sequence of the severe danger suitable for the user.
2. The method for recommending severe illness using gene test results and questionnaire of claim 1, wherein in step S1, the gene risk label is obtained by:
(S1-1) if the user carries a risk mutation of a certain disease, obtaining a gene risk signature of the disease;
(S1-2) if the gene of the user calculates that the gene risk value of a certain disease belongs to the top 10% of the population, acquiring the gene risk label of the disease.
3. The method for recommending severe illness using gene test results and questionnaire of claim 1, wherein in step S2, the questionnaire label is obtained as follows:
the questionnaire includes the following questions: birthday, sex, height, weight, blood type, behavior habit, eating habit, health problem, cancer of the immediate relatives;
(S2-1) calculating the age of the user by the birthday to obtain labels of different age zones;
(S2-2) a label for calculating obesity or underweight by height and weight;
(S2-3) acquiring a behavior tag of good or bad health through the behavior habit;
(S2-4) acquiring a favorable or unhealthy diet label through the diet habit;
(S2-5) acquiring an existing disease label through the health issue;
(S2-6) obtaining a parent cancer signature by whether the parent has cancer.
4. The method for recommending severe illness using gene test results and questionnaire of claim 1, wherein in step S3, the method for obtaining the comprehensive label is as follows:
(S3-1) comprehensive high-risk lung cancer label: if the user has a gene risk label of lung cancer, or the person has smoking habit, or the close relatives of the person have the disease history of lung cancer, acquiring the label;
(S3-2) comprehensive cancer high risk signature: obtaining a label if the user has multiple genetic cancer risk labels;
(S3-3) synthesizing a high risk label for coronary heart disease: if the user has any label of the risk of hypertension, hypertriglyceridemia and coronary heart disease, obtaining the label;
(S3-4) synthetic motor neuron disease signature: the signature is obtained if the user has any signature carrying a dystonia risk mutation, tardive dyskinesia, essential tremor, amyotrophic lateral sclerosis, gene progressive supranuclear palsy.
5. The method for recommending a severe risk using a gene test result and questionnaire of claim 1, wherein the method for obtaining the severe risk label in step S4 is as follows:
arranging the sold severe risks and making a label for each severe risk, wherein the label comprises the following steps: suitable sex, weight range, age range, whether some diseases are carried, guaranteed disease type, and reimburseable times.
6. The method for recommending a severe risk using a gene test result and questionnaire of claim 1, wherein the method for calculating the matching degree of the severe risk label in step S5 is as follows:
setting the comprehensive label of the user as D and the comprehensive label conforming to a certain condition or a certain disease as Dn，DnCorresponding weight is Qn(ii) a Setting a heavy disease label as I, and setting a certain heavy disease label on sale as In(ii) a Setting the matching score of the label of the severe disease and the comprehensive label as S, InComprehensive label D of coveragenHas a matching score of Sn＝In(D1，D2，D3……Dn)；
Calculating the matching score S of a certain disease risk and the comprehensive labeln＝D1*Q1+D2*Q2+D3*Q3+……+Dn*QnI.e. covering D for the severe risk firstnSetting the weight QnAfter that, the summation is performed.
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